Table REF  presents the results of our CNN-based code generation, in comparison with previous state-of-the-art models: (1) Latent Predictor Network [1]}, an enhanced sequence-to-sequence model with multiple token-level predictors; (2) SEQ2TREE [2]}, a sequence-to-sequence model based on AST; (3) Syntactic Neural Model [3]}, an LSTM decoder based on AST structures; and (4) Abstract Syntax Networks [4]}, another AST-based sequence-to-sequence model, which builds two LSTMs predicting rules in the horizontal and vertical directions, respectively. The ASN model has a variant (ASN+SUPATT) where attention is trained in a supervised fashion.
